- Neuromorphic Computing - Home
- Neuromorphic Computing - Introduction
- Neuromorphic Computing - Difference From Traditional Computing
- Neuromorphic Computing - History and Evolution
- Neuromorphic Computing - Types of Technologies
- Neuromorphic Computing - Architecture
- Neuromorphic Computing - Memristors
- Neuromorphic Computing - Synaptic Devices
- Neuromorphic Computing - Hardware Accelerators
- Neuromorphic Computing - Neuromorphic Chips
- Neuromorphic Computing - Analog Circuits
- Neuromorphic Algorithms and Programming
- Neuromorphic Computing - Spiking Neural Networks (SNNs)
- Neuromorphic Computing - Algorithms for SNNs
- Neuromorphic Computing - Programming Paradigms
- Applications of Neuromorphic Computing
- Neuromorphic Computing - Edge Computing
- Neuromorphic Computing - IoT
- Neuromorphic Computing - Robotics
- Neuromorphic Computing - Autonomous Systems
- Neuromorphic Computing - AI and ML
- Neuromorphic Computing - Cognitive Computing
- Neuromorphic Computing Resources
- Neuromorphic Computing - Useful Resources
- Neuromorphic Computing - Discussion
Neuromorphic Computing - Hardware Accelerators
In Neuromorphic Systems, several types of hardware accelerators are used to simulate behavior of natural neural networks. Memristor-based accelerators, Application-Specific Integrated Circuits and Field-Programmable Gate Arrays are some of the commonly used neuromorphic hardware accelerators. In this section we will discuss all the types of hardware accelerators for neuromorphic systems, its features and examples.
Neuromorphic Processing Units
Neuromorphic Processing Units (NPUs) are special processors made to function like the neural networks in the human brain. The cognitive part of human brain uses Spiking Neural Networks learn and adapt to new environments, this is the same technology used in NPUs.
Features
- Spiking Neural Network: NPUs uses Spiking Neural Network, a special type of neural network that work same as biological neurons by using discrete, time-dependent spikes to transmit and process information.
- Energy Efficiency: NPUs are optimized to handle neural computations with minimal energy usage, Hence it can be used for systems with large-scale parallelism.
- Adaptability: NPUs neural structure will adapt to changing environments and input data by dynamically reconfiguring their synaptic connections. This is the same way our brain learn and adapt to new environments.
Example
- IBM TrueNorth: A neuromorphic chip designed using million neurons and billions of synapses. This will provide highly parallel and efficient processing capabilities.
Field-Programmable Gate Arrays (FPGA)
Field-Programmable Gate Arrays (FPGAs) are reconfigurable hardware devices that can be programmed to implement custom neural network architectures. These are useful for prototyping and experimenting with different neuromorphic computing models.
Features
- Reconfigurability: FPGAs can be reprogrammed to support different neural network topologies and computational models.
- Parallel Processing: FPGAs is good at executing multiple computations in parallel, which is essential for simulating large neural networks.
- Energy Efficiency: FPGAs can optimize power usage by changing hardware configurations to specific neural models.
Example
- Xilinx UltraScale+: It's developed by Xilinx (now part of AMD), known for its high performance and advanced capabilities. It's suitable for a wide range of applications in telecommunications, data centers, automotive, aerospace, and industrial applications.
Application-Specific Integrated Circuits (ASICs)
Application-Specific Integrated Circuits (ASICs) are custom-built chips designed for specific neuromorphic applications such as deep learning and neural simulation.
Features
- High Efficiency: ASICs known for good computational efficiency because of it's tailoring to perform specific neural processing tasks.
- Low Power Consumption: These circuits are optimized for energy-efficient operation, which is critical in power-sensitive neuromorphic systems.
- Compact Design: ASICs are compact and designed for large-scale integration.
Example
- Intel Loihi: A neuromorphic ASIC designed to simulate spiking neural networks. This is energy-efficient and scalable neural processor.
Memristor-Based Accelerators
Memristor-based accelerators use memristors to store and process information in the form of resistance changes. These systems are known for highly parallel computations and efficient memory usage.
Features
- Non-Volatile Memory: Memristor-based accelerators can retain information even when powered off. Hence it is ideal for energy-efficient, continuous learning systems.
- Analog Data Storage: Memristors store data in analog form using decimal values of resistance. This way it will allow for more nuanced data representation compared to traditional binary systems.
- Scalability: Memristor-based systems can scale to large numbers of neurons and synapses.
Example
- HP Memristor Crossbar Array: A memristor-based system designed to accelerate neuromorphic computing tasks such as pattern recognition and real-time learning.
Analog Neural Chips
Analog neural chips are specialized hardware designed to process information in a continuous and analog fashion, same as the behavior of biological neurons. These chips are used to model real-time neural computations in neuromorphic systems.
Features
- Continuous Signal Processing: Analog neural chips process data in a continuous range, hence it can generate immediate responses for simulations and control systems.
- Low Power Usage: These chips consume significantly less power, making them ideal for large-scale, real-time neural networks.
- Real-Time Adaptation: Analog neural chips can quickly adapt to changing inputs just like how our brain learn and adapt to new environments.
Example
- BrainChip Akida: A neuromorphic analog neural chip used for real-time edge computing and pattern recognition tasks.